A narrative review of the safety concerns of deprescribing in older adults and strategies to mitigate potential harms
Bibliographic record
Abstract
INTRODUCTION: As with prescribing or continuing medications, deprescribing brings with it the potential for harm as well as benefit. Uncertainty and avoidance of harm has been reported as a barrier to deprescribing in practice and may contribute to continuation of inappropriate medications. AREAS COVERED: This narrative review covers four main safety concerns/potential harms of deprescribing in older adults: adverse drug withdrawal events, return of medical condition(s), reversal of drug-drug interactions and damage to the doctor-patient relationship. These are discussed in relation to medications in general, with some examples of medication classes used to illustrate the potential safety concerns. The majority of these harms can be minimized or even prevented by using a patient-centered, structured deprescribing process with planning, tapering and close monitoring during, and after medication withdrawal. EXPERT OPINION: More research is needed into the safety concerns of deprescribing, however, avenues exist during drug development and post-marketing surveillance to gain knowledge on this topic. Questions remain about when it is suitable to discontinue certain medications/medication classes and there is uncertainty about the harms and benefits of both medication continuation and discontinuation in complex older adults.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".